“It was the best of times, it was the worst of times…”
As part of our beatonbenchmarks reports, we report to firms how they are performing in key service attributes. These cover the full breadth of client experience and can be used to predict other performance metrics. Most often we do this by reporting on the average score of their client base in each attribute. Knowing where they are ahead of or behind their competitors provides firms with diagnostic information for improving their overall client service and competitiveness.
For many firms, however, knowing the average score does not tell the whole story. In this example of a large consulting engineering firm, if we take the distribution of ratings for each attribute and lay them on top of each other, we get the striking chart in Figure 1.
Figure 1. Distribution of client ratings for each attribute overlayed
In statistics, this distribution is known as bi-modal, i.e. there are clearly two peaks. When this occurs it usually means you have two populations in your data. In this case we have people who rate many attributes positively and those who rate the same attributes negatively. By what measure can we try to separate these two populations?
Net Promoter Score
As my colleagues have written previously, we here at beaton are strong advocates for using Net Promoter Score (NPS) as one measure of client satisfaction. The NPS is calculated based on asking respondents “What is the likelihood that you would recommend firm X to a friend or business colleague?”
Those who score between 0-6 are classified in the NPS as being Detractors. These are individuals who not only would not recommend your firm but will readily volunteer their negative assessments to their peers. A handful of Detractors or Dissatisfied clients may have marginal negative effects on your firm’s future project pipeline, but if it becomes a systemic problem then the reputational damage can be severe and may eliminate your firm from consideration by prospective clients.
If we take Figure 1 and segment the respondents into Dissatisfied and Satisfied clients, we get Figure 2. Using Detractors almost perfectly explains the two distinct peaks in the original chart. What this also tells us is that when you have Detractors, they will generally be dissatisfied with most attributes.
Figure 2. Distribution of client ratings on attributes segmented by likelihood of recommendation
What can be done?
Does this mean in order to address your Dissatisfied clients you take a scattershot approach and try to improve your scores on all your attributes at the same time? Firms that have the most success using beatonbenchmarks to implement client improvement strategies are those that use targeted efforts, focusing on the most important drivers of our proprietary Overall Client Service (OCS) measure. They apply their scare resources where the impact is likely to be greatest.
In Figure 3 we show the relationship between OCS and the likelihood of being recommended by a client. Perhaps unsurprisingly there is a very, very clear relationship between these two measures. Improving OCS is an effective pathway to improving likelihood of recommendation.
Figure 3. Strong positive relationship between likelihood of recommendation and OCS
At beaton we have launched a new product called the Dissatisfied Clients Diagnostic that guides your firm on this pathway. We measure who your Dissatisfied clients are (fully within the letter and spirit of Privacy law), what they are unhappy about and we analyse their reasons for why they are dissatisfied. By combining this diagnosis and the predictive analysis of OCS that is part of the beatonbenchmarks report, a plan is informed with much greater precision that would otherwise be possible.
To further enhance the utility of this report, we further segment the analysis by your core or key clients. Knowing how many of those who are dissatisfied are from your core client organisations is invaluable for firms in a time when clients have ever-more competitive choices.
From Detractors to Neutrals
Implementation of this plan means a lift in your OCS score and thereby a lift in how likely a client is to recommend you. However, is this enough? There is an old adage that a happy customer tells one other, but an unhappy customer tells ten. You may be able to lift the 5s and 6s on the vertical axis on Figure 3 into the Neutral territory of 7 and 8s, but some may be too unhappy for any intervention.
To offset the negative word-of-mouth effects of these clients, special efforts must be made to move your current 7s and 8s up into the Promoters zone. Promoter clients are proud advocates for your service and value.
Our Dissatisfied Clients Diagnostic also includes an analysis of your Satisfied clients, elucidating about what they are most happy. This analysis is further enhanced by knowing if and how your key satisfied clients differ from your other satisfied clients.
Focusing on these targeted attributes can improve your firm’s client centricity and its Competitive and Relationship NPS, as my colleague Paul Hugh-Jones has written. This report and subsequent interventions, however, are ‘after the fact’ and though important from a holistic perspective, it would be far better to have discovered this client dissatisfaction sooner. To that end, to measure this Transactional NPS, beaton offers beatondebrief, our granular, interactive tool for client feedback at a project or matter level.
Doing nothing is not an option
Your Dissatisfied clients can be harming your firm’s brand and impacting on your future business. beaton's Dissatisfied Clients Diagnostic helps you systematically assess the problem and informs a plan of action for how to best improve your client service. Since we launched the Dissatisfied Clients Diagnostic nine firms have benefited, with many remarking that it provides a comprehensive framework for dealing with unhappy clients that hitherto has depended on anecdotal evidence.
Shanan Kan is beaton's Product Manager, overseeing the beatoncompass reports on trending topics using clients’ insights to inform firms’ strategies. With a background in psychology, his passions include UX, gameful design and data communication. Connect with him on LinkedIn or reach out on Twitter @ShananKan